Establishes finite-sample regret bounds of order sqrt(N-dim(Π)/N) for IPW and DR estimators in Wasserstein policy learning with distributional outcomes, plus a matching minimax lower bound.
arXiv preprint arXiv:2506.22754 , year=
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A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
IVFR extends global Fréchet regression to endogenous covariates via projection of IV-weighted quantile curves onto valid distributions in 2-Wasserstein space, with weak convergence to a Gaussian process and valid multiplier bootstrap for uniform inference.
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Wasserstein Policy Learning for Distributional Outcomes
Establishes finite-sample regret bounds of order sqrt(N-dim(Π)/N) for IPW and DR estimators in Wasserstein policy learning with distributional outcomes, plus a matching minimax lower bound.
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Bayesian Global Fr\'echet Regression via Weak Conditional Expectations
A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.